Simultaneous binocular assessment of multiple optic nerve and cortical regions in diseases affecting nerve condition

ABSTRACT

This invention concerns the detection of neural damage to the optic nerve, optic radiations and white matter of the visual brain indicative of various neurological disorders but with particular application to multiple sclerosis. The method involves simultaneous measurement of the shape and latency of responses to visual stimuli presented simultaneously in different parts of the visual field of the two eyes, separating out linear and nonlinear response components, and most particularly separating out binocular interaction responses to assess neural function within each half of the visual cortex. Because rough retino-topicity is maintained in the optic nerve, through the optic radiations and the visual cerebral cortex, stimulation of the separate parts of the visual field permits isolation of responses from different component parts of these neural elements and therefore from the results obtained an objective assessment of damage to these separate neural components can be made simultaneously.

TECHNICAL FIELD

This invention concerns the detection of neural damage to the opticnerve, optic radiations and white matter of the visual brain indicativeof various neurological disorders but with particular application tomultiple sclerosis.

BACKGROUND ART

Diseases such as multiple sclerosis reduce the effectiveness of neuraltransmission. A common diagnostic technique in these diseases is tomeasure electrical potentials evoked in response to various forms ofstimulation of peripheral sensory nerves: abnormally long neuralconduction times being equated with neural disease. Neurones concernedwith conducting neural signals over longer distances are typicallyassisted in their conduction by a sheath of insulative material known asmyelin. Diseases such as multiple sclerosis disrupt the myelin sheathand thereby impair the speed of neural conduction. Such changes in nerveconduction latency and other changes in the time-course of the evokedpotential can be observed in a variety of other disorders such as opticneuritis, various optic atrophies, toxic amblyopia, papilledema,Parkinson's, tumours, migraine, various ataxias, compression of thevisual nerves, spinocerebellar degenerations and Vitamin B₁₂ deficiencyas described in the paper by S. Sokol, entitled “The visually evokedcortical potential in the optic nerve and visual pathway disorders”,which was published in Electrophysiological testing in diseases of theretina, ootic nerve, and visual pathway, edited by G. A. Fishman,published by the American Academy of Ophthalmology, of San Francisco, in1990, Volume 2, Pages 105-141.

The evoked potentials (EPs) conventionally measured reflect the activityof large bundles of neurones contained within a nerve. Thus the EPrepresents a gross sum of many cells' activity. Such a gross sum wouldtend to mask the effects of small focal lesions to smaller subsets ofneurones within the nerve, such as those found in multiple sclerosis,since responses from neurones with damaged and intact myelin are summedtogether. Therefore it would be desirable to obtain different EPresponses produced by different component parts of a nerve in order tohighlight focal neural damage.

In more recent times Magnetic Resonance Imaging has provided a method toobtain images at least of the larger focal lesions found in multiplesclerosis. Studies comparing the relative sensitivities andspecificities of MRI with a variety of Evoked Potential (EP) methods,including Visual (VEP), Auditory (AEP) and Somatosensory (SEP) methods,reveal that MRI is superior to the EP methods in diagnosing MS asdescribed in the paper by T. Sand and I.A. Sulg, entitled “Evokedpotentials and CSF-immunoglobulins in MS: relationship to diseaseduration, disability, and functional status”, which was published inActa Neural Scand, Volume 82, Pages 217-21, and the paper by HI.A vanDiemen, P. Lanting, J.C. Koetsier, R.L. Strijers, H.K van Walbeek andC.H. Pornan, entitled “Evaluation of the visual system in multiplesclerosis, a comparative study of diagnostic tests”, which was publishedin Clin Neurol Neurosurge, Volume 94, Pages 191-5. Of these various EPmethods the VEP comes closest to matching the performance of MRI₁, VEPsensitivity rarely lagging MRI by more than 10% as described in thepaper by M. Ravnborg, R. Liguori, P. Christiansen, H. Larsson and P.S.Sorensen, entitled “The diagnostic reliability of magnetically evokedmotor potentials in multiple sclerosis”, which was published inNeurology. Volume 42, Pages 1296-301. As pointed out above part of thefailing of the EP methods is undoubtedly that the measured potentialscommonly represent a sum over the whole of whichever particular sensorypathway is stimulated. In the case of the VEP some differentialmeasurement is often attempted by using stimuli consisting ofchecker-board patterns of different scales, the idea being that finerpatterns bias the VEP somewhat towards measurements from the centralretina and visual field as described in the paper by M.R. Harter,entitled “Evoked cortical responses to checkerboard patterns; effect ofcheck-size as a function of retinal eccentricity”, which was publishedin Vision Res, Volume 10, Pages 1365-76. Attempts have been made tocharacterise responses from each hemifield separately and to try toachieve some separation of hemispheric responses by use of widelydisplaced pairs of electrodes as reported in the paper by L.D.Blumhardt, G. Barrett, A.M. Halliday and A. Kriss, entitled “The effectof experimental ‘scotomata’on the ipsilateral and contralateralresponses to pattern-reversal in one half-field”, which was published inClin. Neurophvsiol., Volume 45, Pages 376-392. Inadequate isolation ofthese responses, in conjunction with different recording electronics anddifferent recording times for the compared responses contribute to lessthan satisfactory results. Nevertheless, given the close concordancebetween VEP and MRI₁, an improved VEP, provides the best promise ofperformance comparable to MRI that could be done in the averageneurologist's surgery as often as desired and at lower cost.

DISCLOSURE OF THE INVENTION

The prime objective of the present invention is the provision of asimultaneous, rapid, reliable test for damage to nerve conduction incomponent parts of the optic nerve, optic radiations and visual cortex.This objective is achieved by presenting visual stimuli, to parts of thevisual field such that component parts of the optic nerve, opticradiations and visual cortex are roughly separately stimulated and withthe temporal structure of the stimuli applied to each visual fieldregion being sufficiently complex in their temporal characteristics topermit estimation of linear and nonlinear weighting functions such asWiener or Volterra kernels to characterise the linear (first order) andnonlinear (second order) responses of each component part of the opticnerve, optic radiations and visual cortex.

In broad form the present invention provides a method of simultaneouslyassessing the presence of damage to component parts of the optic nerve,optic radiations and visual cortex, the method including the steps of.

(a) dividing the visual field of view of each eye into a plurality ofzones so as to roughly isolate confluent streams within the optic nerve,optic radiations and visual cortex due to their retinotopic arrangement;

(b) presenting to the two eyes stimuli having different temporalmodulation of the appearance of each of the zones of the visual field ofeach eye, the stimuli being different for each of the correspondingzones within the visual field of view of each eye,

(c) making the temporal content of the variations of the appearance ofthe time varying stimuli sufficiently complex as to permit estimation oflinear and nonlinear weighting functions characterising measuredresponses to each stimulus region and for each eye;

(d) estimating some or all of the coefficients of the linear andnonlinear functions, for each stimulus zone, and binocular interaction,from the measured responses to said stimuli, to isolate separatecontributions from component parts of the optic nerve, optic radiationsand the left and right halves of the visual brain simultaneously.

Preferably, the linear and nonlinear weighting functions are Weiner orVolterra kernels.

As will be apparent the test involves measuring kernels whichcharacterise the linear and nonlinear responses of component parts ofthe optic nerve, optic radiations and visual cortex in response tosimultaneous stimulation of different parts of the visual field. Theinvention provides for the measurement of nerve conduction information,such as the conduction delay to the peak measured kernel response, forboth first and second order responding nerve components and computationof binocular interaction kernels to isolate simultaneously generatedkernel responses to dichoptic stimulation of the two eyes with differenttemporal sequences of light flashes whose temporal structure issufficiently complex to permit calculation of the requisite kernels.Other aspects of the time-course of the kernels obtained for each partof the optic nerve, optic radiations and visual cortex or comparisonsbetween the responses obtained between component responses can also beutilised. It is important to note that the present invention allowsseparate assessment of visual cortical responses and those from theoptic nerve and optic radiations.

The method can be further extended by simultaneous presentation ofstimuli through other sensory modalities such as tactile or auditorystimuli, having similar temporal characteristics, and computinginteraction kernels between the modalities to isolate responses frombrain regions where these sensory modalities interact, Visualstimulation is of primary interest because of the large number ofneurones in the visual pathway and the close concordance between VEP andMRI as compared to EPs from other sensory modalities.

In a preferred method the visual field is divided into a zone centred onthe central 6 to 12 degrees of the central visual field of view, and atleast 4 surrounding zones splitting the more peripheral visual fieldinto quadrants defining the superior-nasal, superior-temporal,inferior-temporal and inferior nasal peripheral visual field.

Preferably, the latency to selected peaks within time course of thelinear kernels is used as a measure of nerve conduction time with thecomponent part of the optic nerve and optic radiations. Other measuresof the structure or shape of the kernel can also be used.

In a further preferred form, the latency to some or all of the peaksalong the diagonals of the second order self quadratic kernels, and orselected portions of higher order kernels can be used to characterisedifferent nonlinear responses such as ON-OFF responses characteristic ofsub-populations of neurones within the optic nerve. This is possiblebecause each diagonal characterises interactions between pairs ofstimuli presented at different mutual delays.

Preferably, estimated binocular interaction kernels are compared withthose obtained from normal subjects to identify nerve conduction defectswhich would be characteristic of damage within each hemicortex.

In a preferred method, the computation of hemicortical delays isrepeated separately for the separate zones, more preferably the inferiorand superior zone of each half of the visual field.

The estimates of the kernel coefficients are preferably obtained bymeans of logistic or linear regression and or iterative methodspermitting estimates of the errors in the coefficients to be made.Preferably, the coefficients are expressed as measures such ast-statistics rather than physical measures with units such as volts.

In a preferred method, the temporal stimulus sequences will modulate thebrightness of elements within each of the stimulus zones between two orthree brightness levels where the function governing the alternationbetween the levels is approximately uniformly distributed noise.

In accordance with a further preference the stimuli can be extended toother sensory modalities presented simultaneously, where the temporalcharacteristics of these other stimuli are like those of each of thevisual stimuli and interaction kernels between each of the visual fieldzone stimuli and the other sensory modalities, and each of the othermodalities, are also computed to isolate brain responses from brainregions where those sensory modalities interact.

Also, persons skilled in the art will recognise that evoked neuronalresponses may be recorded by means other than by measuring electricalpotentials such as by recording changes in magnetic, or electromagneticradiation, or acoustic signals.

The invention will be further described, by way of example only, withreference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically shows how the visual field maps onto the retina.

FIG. 2 shows a schematic outline of the basic system components formingan apparatus for simultaneously assessing the effectiveness of neuralconduction within the various parts of the optic nerve, optic radiationsand visual cortex according to this invention.

FIG. 3 shows a schematic representation of the visual stimulus used inthe apparatus of this invention.

FIG. 4 shows the linear kernels (solid lines) and the first off-diagonal(dashed-lines), of the matrix of second order kernel coefficientsestimated according to this invention for a normal subject, FIG. 4(a)corresponds to the left eye. FIG. 4(c) corresponds to the right eye.FIG. 4(b) shows the binocular interaction kernels (solid lines).

FIG. 5 shows similar results to FIG.4 using the same normal subject butdifferent visual stimuli.

FIG. 6 shows results similar to FIGS. 4 and 5 for a person affected bymultiple sclerosis.

BEST MODES FOR CARRYING OUT THE INVENTION

As noted above VEPs would likely be enhanced if separate measures couldbe obtained from different component parts of the optic nerve.Fortunately when the nerves of the retina exit the eye they maintainmuch of their relative positions within the nerve. Thus a transection ofthe nerve would show that the nerves within a given quadrant of thecircular cross section would come from the same quadrant of the eye asindicated in FIG. 1. Therefore stimulating a given part of the visualfield stimulates a given part of the retina and so in turn a subsectionof the optic nerve. This maintenance of retinal positional order of thecomponent neurones is known as retinotopicity.

The nerves making up the optic nerve are also not all of the same typebut come in several classes. A major differentiating feature of some ofthese classes is the relative linearity or nonlinearity of theirresponses to light and also their neural conduction speed as describedin the paper by E. Kaplan and R. M. Shapley, entitled “The primateretina contains two types of ganglion cells, with high and low contrastsensitivity”, which was published in Proc. Nati. Acad. Sci. USA, Volume83, Pages 2755-2557, and the paper by R. T. Marrocco, J. W. McClurkinand R. A. Young, entitled “Spatial summation and conduction latencyclassification of cells of the lateral genicufate nucleus of macaques”,which was published in J. Neuroscience, Volume 2, Pages 1275-1291. Thus,the gross EP potential is the sum of component potentials from neuroneshaving different conduction delays. Separation of linearly andnonlinearly responding neurones would thus further disentangle thesignals summed in the gross VEP.

Another consideration is that the neurones from optic nerves of the twoeyes can have different destinations in the brain. In particular theoptic nerves meet at the chiasm and then reassemble into the opticradiations before reaching the cerebral cortex. Some neurones cross toenter the cerebral cortex on the opposite side of the head while otherscontinue straight to the cerebral cortex of the same side of the head.FIG. 1 schematically shows how the visual field, see rectangle at top offigure labelled visual field, when divided into the central visual fieldand the four surrounding quadrants superior nasal, superior temporal,inferior temporal and inferior nasal, maps onto the retina. Theconfluence of optic nerve fibres then maintain a rough retinotopic mapas they travel into the optic nerve and then, following the chiasm, theoptic radiations and the two visual hemi-cortices. The effect of thisdecussation is that neurones receiving visual stimulation from the leftvisual field of both eyes travel to the right visual cortex, whileneurones receiving visual stimulation from the right visual field ofboth eyes travel to the left visual cortex. The decussation of the opticnerves permits the binocular interactions required for the perception ofdepth through stereoscopic vision. The binocular interactions in thecortex pose problems for persons measuring VEPs given that typicallyonly one electrode set is used and isolation of responses on the twosides of the brain is little improved by the use of further electrodesplaced at different locations on the scalp. It would therefore be highlydesirable to separate out binocular neural response associated withbinocular function within each of the two sides of the cortex and assessthose responses for conduction delays and other defects produced byimpaired neural transmission brought about by diseases such as multiplesclerosis.

Linear systems can be fully characterised by the response of the systemto a brief impulsive stimulus. The resulting impulse response or kernelcan then be used to accurately predict the response of the system to anyarbitrary stimulus by simply convolving the stimulus sequence with thekernel. The kernel thus embodies a complete description of the system.Nonlinear systems cannot be characterised in this way because thenonlinearities will lead to interactions between stimulus componentsthat cannot be characterised by the response of the system to a singleevent. All pairwise interactions could be characterised by testing withevery possible pair of stimuli. In the case of a flashing light stimulusin a visual system this would correspond to testing every possible setof latencies between two flashes to look for facilitation and ON-OFFresponses. Responses are said to be ON-OFF if equal responses areobtained to the onset and offset of a flash, as would be generated by aquadratic response nonlinearity. Obviously such an exhaustive searchwould be very time consuming and would leave the difficulty of how topiece all that information together to predict the response of thenonlinear system to an arbitrary stimulus to demonstrate that thenonlinear system had been adequately characterised.

A far better situation-would be if quite arbitrary stimulus sequencescould be presented which would permit information about the response ofthe nonlinear system to be encapsulated in something like the kernel ofa linear system. Ideally, any linear and nonlinear response componentsalso should be separately characterisable. Nonlinear SystemsIdentification (NSI) provides exactly that. In particular, methods whichpermit the estimation of Wiener Kernels provide a so called orthogonaldecomposition where there is no correlation between the kernelscharacterising linear, quadratic, cubic etc. interactions of the systemunder study. The method also permits multiple stimuli to be presented,and separate kernels describing the linear and nonlinear responses toeach stimulus, and further kernels describing any interactions betweenthe stimuli, to be obtained. These kernels are explained more formallybelow.

The following equation defines the model response of a linear systemreceiving a single input stimulus: $\begin{matrix}{{r(t)} = {\int_{0}^{M}{{k\left( t_{1} \right)}{s\quad\left( {t - t_{1}} \right)}{t_{1}}}}} & (1)\end{matrix}$

where r(t) is the response at time t, s(t−t₁) is the stimulus atpreceding time t−t₁, k(t₁) is the kernel value, giving the weight forthe lag t₁, and weighted stimulus values are integrated from lag zero upto the effective memory length of the system, M.

This archetypical system model is elaborated in the following ways.First, a number of input stimulus signals are considered, correspondingto the different zones of visual stimulation, and to stimulation via theleft and right eyes, presented by the dichoptic method. If the stimuluschannels are indexed as s₁, s₂, . . . , s_(n), the first-order responsecomponent is now: $\begin{matrix}{{r_{1}(t)} = {\sum\limits_{i = 1}^{n}{\int_{0}^{M}{{k_{i}\left( t_{1} \right)}{s_{i}\quad\left( {t - t_{1}} \right)}{t_{1}}}}}} & (2)\end{matrix}$

Secondly, response components are added which are second-order, orbilinear, in each of the stimulus signals: $\begin{matrix}{{r_{1}(t)} = {\sum\limits_{i = 1}^{n}{\int_{0}^{M}{\int_{0}^{M}{{k_{li}\left( {t_{1},t_{2}} \right)}{s_{i}\left( {t - t_{1}} \right)}{s_{i}\left( {t - t_{2}} \right)}\quad {t_{1}}\quad {t_{2}}}}}}} & (3)\end{matrix}$

where k_(ii)(t₁,t₂) is the second-order kernel, weighting the stimulusproduct s(t−t₁) s(t−t₂), for the pair of lag values t₁,t₂, and theweighted stimulus values are integrated over all pairs of lags, t₁,t₂from zero up to the effective memory-length, M.

Thirdly, interaction terms are added modelling second-order interactionbetween certain of the stimulus channels, in particular between thecorresponding regions of visual field presented via left and right eye.For channels s_(i), and s_(j), the interaction term is: $\begin{matrix}{{r_{ij}(i)} = {\int_{0}^{M}{\int_{0}^{M}{{k_{ij}\left( {t_{1},t_{2}} \right)}{s_{i}\left( {t - t_{1}} \right)}{s_{j}\left( {t - t_{2}} \right)}\quad {t_{1}}\quad {t_{2}}}}}} & (4)\end{matrix}$

These kernels, k_(ij) are called the Binocular Interaction Kernels, orBIKs. As the signals from each eye first come together in the visualcortex, the BIKs reflect signal transformation that can be inferred tobe at the cortical level, and not earlier in the optic, nerve or opticradiations.

The resulting model, formed by summing all these terms, relates theapplied stimulus signals, with the recorded response, r(t), by means ofunknown weighting functions, or kernels, k_(i), k_(ii), and k_(ij). Theresulting model, whilst nonlinear in the stimulus signals due to thesecond order products, is still linear in all kernel values. Linearregression techniques are used to form the least-squares estimates ofthese kernels, at the sampling interval used in recording, thuscharacterising the relative latency and sensitivity of response to thestimulus fields of each eye, and also the second-order response to eachregion, and the interaction term between corresponding regions of eacheye. By extension it can be seen that higher order kernelscharacterising higher order response nonlinearities may also beestimated.

Considering firstly stimuli presented over time the first order (linear)kernels k_(i), are one dimensional functions, as can be seen fromequation 2. In the case of flashes presented to the component parts ofthe visual field the kernels obtained are like the impulse responses ofthe visual system in the sense that convolution with an arbitrarystimulus yields a highly accurate prediction of the linear response ofthe system to the arbitrary stimulus sequence. The shape of the kernelprovides information about the latency and gain of the linear responsejust as the response to a repeated train of short flashes would exceptthat any nonlinear response that might confound interpretation has beremoved.

The second order kernels (k_(ii)) are 2 dimensional, as can be seen fromEquation 3: given that they are sets of numbers character within a setduration, that is the memory length (M) of the system, As such k_(ii)would for example characterise ON-OFF responses. To obtain the fullpredicted response to an arbitrary stimulus these two traces, obtainedby convolutions between that stimulus and k_(i) and k_(ii), are simplyadded.

It is apparent that even such a simple characterisation would be animprovement over a normal VEP in that separate latencies for largelylinear and largely ON-OFF ganglion cells in the optic nerve could beobtained. One limitation is that the number of coefficients estimated inthe various kernels should not exceed the number of stimulus eventsobtained in the recording process. Thus, it is best to keep the numberof regions small. Methods such as linear regression or iterative methodscan be used to estimate only a selected subset of the coefficients tokeep their number small.

Such a stimulus system is in part the basis of the proposed method anddevice with the added innovative feature of exploiting theretinotopicity of the optic nerve and optic radiations to obtainseparate kernels for each component part of the nerve and the furtherinnovative feature of dichoptic presentation of different stimuli toeach eye for the purpose of simultaneously obtaining kernels for each ofthe left and right halves of the cerebral cortex, that is for eachhemicortex. To achieve these objectives the visual field is split intonasal and temporal stimulus subregions permitting the possibility ofmeasuring separate signals for conduction within each hemicortex giventhat information from the left visual field of the two eyes is combinedfor the first time in the right hemicortex, as shown in FIG.1, and visaversa for the right visual field. To achieve this separate signals aresimultaneously provided to the two eyes.

This can be achieved by use of a system such as a liquid crystal frameshutter. The shutter covers the whole of the visual display device andtransmits left or right circularly polarised light for each alternateframe in the stimulus sequence. The subjects wear passive glassescontaining a quarterwave plate and dichroic filters. This system permitsseparate video images to be sent to both eyes and the degree ofisolation of the dichoptic stimuli is preserved even with head rotation.Thus on each alternate frame differing, temporally orthogonal signalswill be sent to each eye for each region. With this stimulus system itis possible to calculate from the recorded evoked response, binocularinteraction (BIK, pronounced bike) kernels k_(ij) as described inequation 4, for each stimulus region. The retinotopicity of the opticnerve system can be further exploited by splitting the visual field intoinferior and superior parts. A person skilled in the art will appreciatethat different means of generating separate signals for the two eyes,whether these be optical means, employing separate stimulus displaydevices to the two eyes, or by holographic means, can also be used.

In the simplest case of stimuli presented to the left superior andinferior visual fields and the right inferior and superior visual fieldsnotice that the BIK kernels for the two left visual fields characterisea (binocular) signal only found in the right hemicortex (and some partsof the thalamus). Similarly the 2 BIK kernels from the right hemifieldcharacterise nerve conduction processes in the left hemicortex. Thus, acompletely new type of information is provided by the invention. Inpractice one or more separate regions can occupy the central few degreesof the visual field.

In the examples below neural responses are characterised to the stimuliby measuring evoked electrical potentials. Persons skilled in the artwill recognise that evoked neuronal responses may be recorded by meansother than by measuring electrical potentials such as by recordingchanges in magnetic, or electromagnetic radiation, or acoustic signals.

In addition to the BIK data, and temporal interaction data, the methodaccording to this invention permits the measurement of separate latencyand amplitudes for linear and ON-OFF nerve components for severalcomponent parts of the optic nerve and radiations. Separation of thesecomponent signals provides improvement over the gross method ofeffectively averaging all these signal components as currently occurswith conventional VEPs. It is amazing that standard VEPs are nearly aseffective as MRlI the corollary of which is that the new method has thepotential to be as good or better than MRI.

EXAMPLES

FIG. 2 shows a schematic scheme of the basic system components formingan apparatus for simultaneously assessing the effectiveness of neuralconduction within the various parts of the optic nerve, optic radiationsand visual cortex corresponding to different parts of the visual field.The major components are an apparatus for dichoptic stimulation of thetwo eyes, in the present non-limiting example by means of a liquidcrystal shutter, a means for assessing cortical neural responses, in thepresent example electrodes and an amplifier for recording a visualevoked electrical potential, and a means for computing estimates ofkernel coefficients. Thin arrows associate labels with objects whilethick block arrows indicate the direction of information flow orcontrol.

Normal subjects and subjects diagnosed with definite multiple sclerosiswere compared. The test stimuli for each subject were presented on avideo monitor at 101 pictures per second. Since the stimuli werepresented on a video monitor it is common to refer to the sequence ofpictures presented as a sequence of frames presented at a particularframe rate, in this case 101 frames per second. The stimulus sequenceconsisted of a stream of separate, but temporally interleaved, imagespresented alternately to each eye at 50.5 frames per second by use of aliquid crystal shutter. Presentation of separate images to the two eyesis referred to as dichoptic presentation. To achieve dichopticpresentation of the stimuli to the two eyes the liquid crystal shuttertransmitted on alternate frames, light that is left or right circularlypolarised, the changes in polarisation being synchronised to the picturepresentation rate of 101 frames per second. Subjects wore glasses wherethe element covering each eye transmitted only one of the twopolarisations of the light transmitted through the shutter. In this wayeach eye saw only one of the two interleaved video sequences, each eyereceiving pictures at 50.5 frames per second. Subjects also wore normalcorrective lenses as necessary. The total duration of the test sequenceswas seconds and up to 16 sequences were presented to each subject.

Subjects were asked to fixate a spot presented at the centre of thevisual stimulus. Persons skilled in the art will recognise that othermeans of maintaining fixation, such as monitoring eye position couldhave been substituted without affecting the present demonstration.Evoked potentials were recorded with the samples being obtainedsynchronously with the rate of presentation of video stimuli. Fastersampling rates could have been used but for the present demonstrationone sample per frame was used. Standard gold cup electrodes were placedon the scalp to record the evoked potentials. The dichoptic stimulusgeneration scheme and the VEP recording apparatus are illustrated inFIG. 2.

FIG. 3 shows a schematic representation of the visual stimulus used inthe present demonstration, where checkerboard patterns with scalesadjusted to match the magnification of the retinal projections onto thevisual cortex are presented to eight different portions of the visualfield, the separate regions being enumerated 1 to 8 respectively. Thepresent diagram shows the boundaries of the individual checks. Theboundaries of the eight regions are shown by the thicker black lines. Inpractice checkerboard patterns were formed by colouring every othercheck either black or white.

This scaling matches the well known magnification of the retinal neuronsonto the visual cortex. Again, persons skilled in the art will recognisethat other patterns, could have been substituted for the checks, andalternative scalings of the patterns could have been employed. In thepresent demonstration the contrast of the checks was reversed in timeaccording to a pseudorandom sequence. In the present demonstration thepseudorandom sequence was either 1 or −1 at random with 50% probabilityof being in either state. In the one alternative demonstration thechecks were coloured either black or white and the checks the temporalmodulation sequence reversed their contrast. Contrast reversal of agiven checkerboard indicates changing the black regions to white and thewhite regions to black. Mathematically we could represent the darkchecks as having brightness −1 and bright checks as having brightness 1.On each frame of the stimulus sequence the pseudorandom sequence canadopt a value of −1 or 1, thus reversing the sign of the checkerboardcontrast or leaving it unchanged. Kernels estimated with this contrastreversal stimulus are shown in FIG. 4. The visual stimuli employed inthe eight regions of the stimulus reversed contrast in time according toa pseudorandom sequence. The plotted quantities in all cases aret-statistics with the same scaling applied to all the plotted waveforms.The pair of parallel dotted lines in each of FIGS. 4(a), 4(b) and 4(c)indicate the evoked potential amplitude beyond which the kernelcoefficients are significant at the 95% level. The kernel waveformsshown are all the average of kernel estimates obtained over 4 repeats.

In a second alternative demonstration half the checks remained at themean luminance of 50 nits while the alternate checks were modulatedblack or white. This alternative stimulus is referred to as a binaryonset stimulus. Kernels estimated with this binary onset stimulus areshown in FIG. 5. The same normal subject was tested 4 times and thekernels averaged. The onset stimulus enhances the linear kernelamplitude compared to the contrast reversal case shown in FIG. 4 andalso produces more reliable BIK waveforms.

Alternately, more than two brightness states can be used, for example aso-called ternary stimulus where checks can be one of black, white orgrey presented with some set of probabilities can be used. Similarly, 4,5 or some higher number of stimulus levels can be incorporated or therange contrasts employed could be lower than that spanned by the rangeblack to white. Similarly some other image quality such as position,apparent depth or colour may be modulated. It is only important that thestimulus ensemble is sufficiently rich to permit estimation of thekernels by processes such as linear or logistic regression or relatediterative methods: a stimulus will form an adequate basis in themathematical sense when the model sub-space is spanned by the stimulus.

The video display was divided into 8 regions constituting 4 quadrants inboth the central and peripheral visual field as illustrated in FIG. 3.The contrast of the checks within each of these 8 zones or regionschanged coherently, that is all of the checks with a particular regionreversed contrast together as determined by the pseudorandom temporalsequence applied to that region. Different temporal sequences where usedto determine the sequence of contrast reversals in each of the 8 visualstimulus regions. Thus, on each frame of the stimulus sequence thechecks within each of the 8 regions might reverse contrast or remainunchanged. Persons skilled in the art will recognise that partitioningsof the visual field other than the 8 employed for the presentdemonstration could have been employed.

Coefficients for the linear, nonlinear and binocular interaction kernels(Equations 2 to 20 4) were then estimated by a least squares methodpermitting the calculation of error estimates and confidence limits onthe coefficients. Kernel coefficients were estimated with a memorylength, M in equations 1 to 4, of 300 ms, and coefficients for each ofup to 16 repeats of the 30 second duration test sequences were estimatedseparately, kernel coefficients were then averaged across repeats. Othermemory lengths could have been employed without diminishing the presentdemonstration. For the present demonstration the test signals wererepeated, however, in a preferred design different sequences would beused on each repeat and the resulting kernels then averaged just as inthe present demonstration.

Persons skilled in the art will appreciate that the quadratic nonlinearkernels (Equation 3) and the binocular interaction kernels (Equation 4)are 2 dimensional matrices of coefficients, the duration of responselatency progressing along the diagonals of these 2 dimensional matrices.The different diagonals of these kernels represent interactions atdifferent delays. For the present demonstration selected diagonals ofthese matrices of kernel coefficients were estimated by linearregression. These diagonal components are thus one dimensional waveformslike the linear kernels having dimensions on the abscissa of responselatency in seconds, exactly as the linear kernels. Thus, we can presenta set of one dimensional linear and nonlinear kernel waveforms which aresufficient for the present demonstration. Selecting a subset of thekernel coefficients also improves the error estimates for thecoefficients in that the ratio of data points to the number of estimatedcoefficients is larger. In a preferred design all the coefficients ofthe entire quadratic and binocular interaction matrices would beestimated to obtain further information about the state of nerveconduction in a given subject.

FIG. 4 shows kernel waveforms for a normal subject where contrastreversing stimuli were employed. The latencies from the beginning ofeach waveform to the largest peaks in each kernel are indicative of theconduction latencies for each stimulus region and eye. Notice thatdifferent conduction latencies are obtained for different parts of thevisual field implying different conduction latencies for each part ofthe visual field due to the rough retinotopicity maintained in the opticnerve. Compare for example the location of the peaks in the kernelwaveforms for stimulus regions 2, 3, 4 and 6, 7, B. Obviously, if thestimulus regions were too small the relatively poor retinotopicity ofthe optic nerve would prevent independent latency data to be obtainedfor each stimulus. Some of the presently observed heterogeneity of theconduction latency in normal subjects will be due to different durationsof nerve signal transmission within the eye. Nevertheless, the resultsare an obvious improvement over the case of recording a single grossevoked potential for the whole eye where the regional differences wouldbe summed together into a single waveform. The results also show thatresults from the two eyes are similar as would be expected in a normalsubject.

The kernel waveforms shown in FIG. 5 are from the same normal subject asfor FIG. 4. In this case, however, binary onset stimuli were employed.This stimulus arrangement enhanced the linear kernel waveforms and alsothe BIK waveforms, thus providing more and better quality data.

The kernel waveforms presented in FIG. 6 were obtained from a subjectwith multiple sclerosis. The stimulus modality was contrast reversal asin the case of FIG. 4. Sixteen repeats were averaged for this subject.Some of the waveforms, such as those for the left eye for regions 2 and4, show large increases in response latency compared to the data fromnormal subjects. The waveforms from the subject with multiple sclerosisalso show decreased kernel amplitude and distortion of the kernels'shapes, particularly in the right eye kernels. This was consistent withthe clinical presentation of the subject. The distortion may arise froma mixture of delayed and non-delayed nerve conduction within thecomponent part of the optic nerve characterised by a particular kernelwaveform. Kernels from the left and right eyes can either besubstantially the same in terms of their latency, as in the kernelwaveforms for region 8, or can be quite different as best shown by theleft and right eye data for region 6. Of particular interest are the BIKwaveforms for regions 4 and 6. While the delay for the BIK waveform ofregion 4 is much the same as for that for the optic nerve responses inFIGS. 6(a) and 6(b), the BIK waveform for region 6 is much more delayedat around 200 milliseconds, than either of the region 6 optic nervewaveforms in FIGS. 6(a) and 6(b). Thus, BIK waveforms can show increasedresponse latency that can only be ascribed to conduction delays within ahemicortex. Since region 6 corresponds to the lower left half of thevisual field the extra conduction delay is in the upper right hemicortexindicating disease in that brain area.

The kernel waveforms presented in FIGS. 4 to 6 demonstrate that it ispossibie to simultaneously estimate binocular interaction kernels forthe two visual cortices and kernels characterising conduction withinboth of the optic nerves, that supply separate information about thestate of nerve conduction in each of these component parts of the visualnervous system. The present demonstration thus shows that the quitelarge stimulus regions employed permit even the relatively poorretinotopicity of the course of passage of individual nerve fibreswithin the optic nerves to be exploited to provide quite independentdata on nerve conduction for each of the simultaneously presented visualstimuli employed. Even in normal subjects quite different conductionlatencies for different component parts of their optic nerves can beobserved, while subjects whose nerve conduction is affected by diseasestates can show both normal and abnormally long nerve conductionlatencies in one optic nerve or hemicortex. Binocular interactionkernels can also show abnormality independent of latency changesobserved in the optic nerve.

The foregoing describes only some embodiments of the invention andmodifications can be made without departing from the spirit and scope ofthe invention.

What is claimed is:
 1. A method of simultaneously assessing the functionof component parts of the optic nerve, optic radiations and visualcortex, the method comprising the steps of: (a) dividing the visualfield of view of each eye into a plurality of stimulus zones so as toroughly isolate confluent streams within the optic nerve, opticradiations and visual cortex due to their retinotopic arrangement; (b)presenting to the two eyes stimuli having different temporal modulationof the appearance of each of the zones of the visual field of each eye,the stimuli being different for each of the corresponding zones withinthe visual field of view of each eye; (c) making the temporal content ofthe variations of the appearance of the time varying stimulisufficiently complex as to permit estimation of linear and nonlinearweighting functions characterising measured responses to each stimulusregion and for each eye; (d) estimating some or all of the coefficientsof the linear and nonlinear functions, for each stimulus zone, andbinocular interaction, from the measured responses to said stimuli, toisolate separate contributions from component parts of the optic nerve,optic radiations and the left and right halves of the visual brainsimultaneously.
 2. A method as claimed in claim 1 wherein the linear andnonlinear weighting functions are Weiner or Volterra kernels.
 3. Amethod as claimed in claim 2, wherein the visual field is divided into azone centred on the central 6 to 12 degrees of the central visual fieldof view, and at least 4 surrounding zones.
 4. A method as claimed inclaim 3 wherein the surrounding zones split more peripheral visual fieldinto quadrants defining the superior-nasal, superior-temporal, aninferior-temporary and an inferior nasal peripheral visual field.
 5. Amethod as claimed in claim 2, wherein the latency to selected peakswithin time course of the linear kernels is used as a measure of nerveconduction time with the component part of the optic nerve and opticradiations.
 6. A method as claimed in claim 2, wherein the latency tosome or all of the peaks along the diagonals of second order selfquadratic kernels, and/or selected portions of higher order kernels areused to characterise different nonlinear responses.
 7. A method asclaimed in claim 6 wherein the different nonlinear responses are ON-OFFresponses characteristic of sub-populations of neurones within the opticnerve.
 8. A method as claimed in claim 2, wherein estimated binocularinteraction kernels are compared with those obtained from known normalsubjects to identify nerve conduction defects characteristic of damagewithin each hemicortex.
 9. A method as in claim 8, wherein the nerveconduction defects are identified separately for selected stimuluszones.
 10. A method as claimed in claim 9 wherein the selected stimuluszones are the inferior, superior, temporal and nasal zones of each halfof the visual field.
 11. A method as claimed in claim 2, whereinestimates of the kernel coefficients are obtained by means of logisticor linear regression and/or iterative methods.
 12. A method as claimedin claim 11 wherein the coefficients are expressed as t-statistics. 13.A method as claimed in claim 1, wherein the stimulus includes modulationof the brightness of elements within each of the stimulus zones betweentwo or three brightness levels and the function governing thealternation between the levels is approximately uniformly distributednoise.
 14. A method as claimed in claim 13, wherein the stimulusincludes modulation of an additional image parameter selected from thegroup or position, or apparent depth or colour of elements of thestimulus zones between two or three levels and the function governingthe alternation between the levels is approximately uniformlydistributed noise.
 15. A method as claimed in claim 1, further includingsimultaneous stimulating one or more other sensory modalities where thetemporal characteristics of these other stimuli are like those of eachof the visual stimuli and interactions between each of the visual fieldzone stimuli and the other sensory modalities, and each of the othermodalities, are determined to isolate brain responses from brain regionswhere those sensory modalities interact.